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Spatiotemporal topic association detection on tweets

Authors:

Joshua R. TrampierAuthors Info & Claims

SIGSPACIAL '16: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Article No.: 28, Pages 1 - 10

https://doi.org/10.1145/2996913.2996933

Published: 31 October 2016 Publication History

Abstract

The analysis of Twitter data can help to predict or explain many real world phenomena. The relationships among events in the real world can be reflected among the topics on social media. In this paper, we propose the concept of topic association and the associated mining algorithms. Topics with close temporal and spatial relationship may have direct or potential association in the real world. Our goal is to mine such topic associations and show their relationships in different time-region frames. We propose to use the concepts of participation ratio and participation index to measure the closeness among topics and propose a spatiotemporal index to calculate them efficiently. With the topic filtering and the topic combination, we further optimize the mining process and the mining results. The algorithms are evaluated on a Twitter dataset with 27,956,257 tweets.

References

[1]

H. Achrekar, A. Gandhe, R. Lazarus, S.-H. Yu, and B. Liu. Predicting flu trends using twitter data. In INFOCOM WKSHPS, pages 702--707. IEEE, 2011.

[2]

R. Agrawal, R. Srikant, et al. Fast algorithms for mining association rules. In VLDB, volume 1215, pages 487--499, 1994.

Digital Library

[3]

S. Asur and B. A. Huberman. Predicting the future with social media. In WI-IAT, volume 1, pages 492--499. IEEE, 2010.

Digital Library

[4]

C. Budak, T. Georgiou, D. Agrawal, and A. El Abbadi. Geoscope: Online detection of geo-correlated information trends in social networks. VLDB, 7(4):229--240, 2013.

Digital Library

[5]

C. Budak, T. Georgiou, and D. A. A. El Abbadi. Geowatch: Online detection of geo-correlated information trends in social networks. Technical report, Technical report, UCSB, 2013.

[6]

S. Carter, M. Tsagkias, and W. Weerkamp. Twitter hashtags: Joint translation and clustering. ACM WebSci, 2011.

[7]

C. Friedman and R. Sideli. Tolerating spelling errors during patient validation. Computers and Biomedical Research, 25(5):486--509, 1992.

Digital Library

[8]

K. Glasgow and C. Fink. Hashtag lifespan and social networks during the london riots. In Social Computing, Behavioral-Cultural Modeling and Prediction, pages 311--320. Springer, 2013.

Digital Library

[9]

Y. Huang, S. Shekhar, and H. Xiong. Discovering colocation patterns from spatial data sets: a general approach. TKDE, 16(12):1472--1485, 2004.

Digital Library

[10]

K. Y. Kamath, J. Caverlee, K. Lee, and Z. Cheng. Spatio-temporal dynamics of online memes: a study of geo-tagged tweets. In WWW, pages 667--678. International World Wide Web Conferences Steering Committee, 2013.

Digital Library

[11]

S. M. Kywe, T.-A. Hoang, E.-P. Lim, and F. Zhu. On recommending hashtags in twitter networks. In Social Informatics, pages 337--350. Springer, 2012.

Digital Library

[12]

R. Lee, S. Wakamiya, and K. Sumiya. Discovery of unusual regional social activities using geo-tagged microblogs. WWW, 14(4):321--349, 2011.

Digital Library

[13]

A. M. MacEachren, A. C. Robinson, A. Jaiswal, S. Pezanowski, A. Savelyev, J. Blanford, and P. Mitra. Geo-twitter analytics: Applications in crisis management. In 25th International Cartographic Conference, pages 3--8, 2011.

[14]

R. Munro, S. Chawla, and P. Sun. Complex spatial relationships. In ICDM, pages 227--234. IEEE, 2003.

[15]

S. Petrović, M. Osborne, and V. Lavrenko. Streaming first story detection with application to twitter. In NAACL, pages 181--189. Association for Computational Linguistics, 2010.

[16]

T. Sakaki, M. Okazaki, and Y. Matsuo. Earthquake shakes twitter users: real-time event detection by social sensors. In WWW, pages 851--860. ACM, 2010.

Digital Library

[17]

T. Sakaki, M. Okazaki, and Y. Matsuo. Tweet analysis for real-time event detection and earthquake reporting system development. TKDE, 25(4):919--931, 2013.

Digital Library

[18]

A. Schulz, A. Hadjakos, H. Paulheim, J. Nachtwey, and M. Mühlhäuser. A multi-indicator approach for geolocalization of tweets. In ICWSM, 2013.

[19]

S. Shekhar and Y. Huang. Discovering spatial co-location patterns: A summary of results. In SSTD, pages 236--256. Springer, 2001.

[20]

T. Sugitani, M. Shirakawa, T. Hara, and S. Nishio. Detecting local events by analyzing spatiotemporal locality of tweets. In WAINA, pages 191--196. IEEE, 2013.

Digital Library

[21]

R. Tinati, L. Carr, W. Hall, and J. Bentwood. Identifying communicator roles in twitter. In WWW, pages 1161--1168. ACM, 2012.

Digital Library

[22]

O. Tsur and A. Rappoport. What's in a hashtag?: content based prediction of the spread of ideas in microblogging communities. In WSDM, pages 643--652. ACM, 2012.

Digital Library

[23]

S. Vaid, C. B. Jones, H. Joho, and M. Sanderson. Spatio-textual indexing for geographical search on the web. In SSTD, pages 218--235. Springer, 2005.

Digital Library

[24]

L. Yang, T. Sun, M. Zhang, and Q. Mei. We know what@ you# tag: does the dual role affect hashtag adoption? In WWW, pages 261--270. ACM, 2012.

Digital Library

[25]

X. Zhang, N. Mamoulis, D. W. Cheung, and Y. Shou. Fast mining of spatial collocations. In ACM SIGKDD, pages 384--393. ACM, 2004.

Digital Library

[26]

Y. Zhou, X. Xie, C. Wang, Y. Gong, and W.-Y. Ma. Hybrid index structures for location-based web search. In CIKM, pages 155--162. ACM, 2005.

Digital Library

Cited By

Wu YWeng DDeng ZBao JXu MWang ZZheng YDing ZChen W(2021)Towards Better Detection and Analysis of Massive Spatiotemporal Co-Occurrence PatternsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.298322622:6(3387-3402)Online publication date: Jun-2021
https://doi.org/10.1109/TITS.2020.2983226
Kovalchuk MNasonov D(2021)Hashtags: an essential aspect of topic modeling of city events through social media.2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA52953.2021.00255(1594-1599)Online publication date: Dec-2021
https://doi.org/10.1109/ICMLA52953.2021.00255
Arya SDutta H(2021)Revealing the Blackmarket Retweet Game: A Hybrid ApproachCombating Online Hostile Posts in Regional Languages during Emergency Situation10.1007/978-3-030-73696-5_4(30-41)Online publication date: 9-Apr-2021
https://doi.org/10.1007/978-3-030-73696-5_4
Show More Cited By

Index Terms

Spatiotemporal topic association detection on tweets
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Other conferences

SIGSPACIAL '16: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

October 2016

649 pages

ISBN:9781450345897

DOI:10.1145/2996913

General Chairs:
Mohamed Ali
University of Washington, Tacoma
,
Shawn Newsam
University of California, Merced
,
Program Chairs:
Matthias Renz
George Mason University, USA
,
Goce Trajcevski
Northwestern University, USA
,
Siva Ravada
Oracle Corporation, USA

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 October 2016

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U.S. Department of Defense

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SIGSPATIAL'16

SIGSPATIAL'16: 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

October 31 - November 3, 2016

California, Burlingame

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SIGSPACIAL '16 Paper Acceptance Rate 40 of 216 submissions, 19%;

Overall Acceptance Rate 220 of 1,116 submissions, 20%

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Cited By

Wu YWeng DDeng ZBao JXu MWang ZZheng YDing ZChen W(2021)Towards Better Detection and Analysis of Massive Spatiotemporal Co-Occurrence PatternsIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2020.298322622:6(3387-3402)Online publication date: Jun-2021
https://doi.org/10.1109/TITS.2020.2983226
Kovalchuk MNasonov D(2021)Hashtags: an essential aspect of topic modeling of city events through social media.2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA52953.2021.00255(1594-1599)Online publication date: Dec-2021
https://doi.org/10.1109/ICMLA52953.2021.00255
Arya SDutta H(2021)Revealing the Blackmarket Retweet Game: A Hybrid ApproachCombating Online Hostile Posts in Regional Languages during Emergency Situation10.1007/978-3-030-73696-5_4(30-41)Online publication date: 9-Apr-2021
https://doi.org/10.1007/978-3-030-73696-5_4
Tsubouchi KTeraoka TGomi HShimosaka M(2020)Parasitic Location Logging: Estimating Users’ Location from Context of Passersby2020 IEEE International Conference on Pervasive Computing and Communications (PerCom)10.1109/PerCom45495.2020.9127381(1-10)Online publication date: Mar-2020
https://doi.org/10.1109/PerCom45495.2020.9127381
Shah ZDunn A(2019)Event detection on Twitter by mapping unexpected changes in streaming data into a spatiotemporal latticeIEEE Transactions on Big Data10.1109/TBDATA.2019.2948594(1-1)Online publication date: 2019
https://doi.org/10.1109/TBDATA.2019.2948594
Visheratin AMukhina KVisheratina ANasonov DBoukhanovsky A(2018)Multiscale event detection using convolutional quadtrees and adaptive geogridsProceedings of the 2nd ACM SIGSPATIAL Workshop on Analytics for Local Events and News10.1145/3282866.3282867(1-10)Online publication date: 6-Nov-2018
https://dl.acm.org/doi/10.1145/3282866.3282867

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